Open Access

Background: CWS/RMS‐96 was an international multicenter trial with randomization between two therapy arms of the standard four‐drug therapy (vincristine, ifosfamide, adriamycin, dactinomycin [VAIA]) versus an intensified six‐drug regimen (carboplatin, epirubicin, vincristine, dactinomycin, ifosfamide, and etoposide [CEVAIE]) for high‐risk rhabdomyosarcoma (RMS), extraskeletal Ewing sarcoma (EES), and undifferentiated sarcoma (UDS) in children, adolescents, and young adults aiming to improve their survival. Intensified chemotherapy with CEVAIE did not improve outcome. Methods: Patients younger than 21 years with a previously untreated localized HR‐RMS, EES, and UDS were enrolled from Cooperative Weichteilsarkom Studiengruppe (CWS) centers in Germany, Austria, Poland, Switzerland, and from Italian Soft Tissue Sarcoma Committee (STSC) centers. Randomization (1:1) to receive either 9 × 21 days cycles of VAIA or CEVAIE was performed separately in CWS and STSC. Hyperfractionated accelerated radiotherapy (32–44.8 Gy) was added at week 9–12 according to histology and response to chemotherapy. A secondary microscopically complete nonmutilating resection was performed if possible. Primary endpoints were response to chemotherapy, event‐free (EFS) and overall survival (OS). Results: Five hundred fifty‐seven patients (HR‐RMS:n = 416, EES and UDS:n = 141) underwent randomization: VAIA (n = 273) or CEVAIE (n = 284). Radiotherapy was given to 70% of patients in both groups. A secondary resection was performed in 47% and 48% patients, respectively. The 5‐year EFS and OS for the VAIA and CEVAIE treatment arms were 59.8% and 60.8% (p = .89), and 74.2% and 68.3% (p = .16), respectively. No differences in response, toxicity, or second malignancies emerged in the two groups. Conclusion: The use of an intensified regimen failed to show a significant improvement in tumor response and outcome of patients with localized HR‐RMS, EES, and UDS.

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and its qualitative and at times quantitative accuracy—on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.